One-Hour Oral Glucose Tolerance Test for the Postpartum Reclassification of Women With Hyperglycemia in Pregnancy
Bibliographic record
Abstract
OBJECTIVE: The International Diabetes Federation recently endorsed a 1-h oral glucose tolerance test (OGTT) as more convenient than the conventional 2-h OGTT. In practice, women with hyperglycemia in pregnancy are advised to undergo a 2-h OGTT within 6 months after delivery, but this test is often not completed, partly owing to its inconvenience for busy mothers. Recognizing the potential advantage of the 1-h OGTT in this setting, we sought to compare 1-h and 2-h OGTT glucose measurements at 3 months postpartum as predictors of dysglycemia (prediabetes/diabetes) over the first 5 years postpartum. RESEARCH DESIGN AND METHODS: A total of 369 women across a range of glucose tolerance in pregnancy (from normoglycemia to gestational diabetes [GDM]) underwent multisample 2-h 75-g OGTTs at 3 months, 1 year, 3 years, and 5 years postpartum. Glucose measurements from the 3-month OGTT were ranked as predictors of dysglycemia (both criteria) by change in concordance index (CCI) of Cox proportional hazard regression models. RESULTS: At the 3-month OGTT, 1-h glucose identified all but 10 of 70 women concurrently diagnosed with dysglycemia by 2-h glucose, while diagnosing an additional 96 women. The cumulative incidence of dysglycemia progressively increased over 5 years by tertile of 1-h glucose on the 3-month OGTT (P < 0.0001). On regression analyses, the strongest predictor of dysglycemia was 1-h glucose (change in CCI: 16.1%), followed by 2-h glucose (14.9%). In women with GDM, 1-h glucose again emerged as strongest predictor of dysglycemia (13.0%), followed by 2-h glucose (12.8%). CONCLUSIONS: The 1-h OGTT may offer a strategy for increasing rates of postpartum reclassification following hyperglycemia in pregnancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".